Maximizing Free AI API Credits: A Guide for Oracle Database Developers

Artificial Intelligence (AI) and Machine Learning (ML) are transforming how we interact with databases, automate tasks, and derive insights from data. Many AI services offer free API credits to help developers experiment, prototype, and build applications without incurring immediate costs. If you’re an Oracle Database developer, leveraging these free credits can be a game-changer for integrating AI-driven features into your applications.

In this post, we’ll explore:
✅ Top AI APIs with Free Credits (including Oracle’s own AI offerings)
✅ How to Use AI APIs with Oracle Database
✅ Best Practices for Maximizing Free Credits
✅ A Sample Oracle PL/SQL Script to Call an AI API

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1. Best Free AI API Credits for Developers

Here are some of the best AI APIs with free credits that Oracle developers can leverage:

🔹 Oracle AI Services (Free Tier)

Oracle Cloud Infrastructure (OCI) offers free AI/ML services for developers:
– Oracle Database AI (AutoML & Generative AI) – Free tier includes:
– Oracle Autonomous Database (Free Tier) – Supports AI-driven SQL queries.
– Oracle Machine Learning (OML) – Free credits for training models.
– Oracle Generative AI Services – Free API calls for text generation, summarization, and more.
– How to Access?
– Sign up for a free Oracle Cloud account (oci.oracle.com).
– Use Always Free Eligible Services (2 AMUs/month for 12 months).

🔹 Google Vertex AI (Free Tier)

• $300 free credits (enough for ~10,000 API calls).
• Supports text generation, image analysis, and autoML.
• Best for: Natural Language Processing (NLP) and computer vision.

🔹 AWS Bedrock (Free Tier)

• 1 million free inference API calls (varies by model).
• Supports Amazon Titan, Claude, and other foundation models.
• Best for: Chatbots, document analysis, and code generation.

🔹 Azure AI (Free Credits)

• $200 free credits (enough for ~10,000 API calls).
• Includes text analytics, speech recognition, and vision APIs.
• Best for: Enterprise-grade AI integration.

🔹 Hugging Face Inference API (Free Tier)

• Free for small-scale use (pay-as-you-go after limits).
• Best for: Custom LLMs, fine-tuning, and NLP tasks.

🔹 Replicate (Free Tier)

• Free credits for popular AI models (Stable Diffusion, LLMs).
• Best for: Generative AI experiments.

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2. How to Use AI APIs with Oracle Database

Integrating AI APIs with Oracle Database can enhance:
✔ Automated data insights (e.g., summarizing large text fields)
✔ Chatbots & virtual assistants (using NLP)
✔ Anomaly detection (fraud, predictive maintenance)
✔ Code generation & SQL optimization

🔹 Approach 1: Call AI APIs from PL/SQL (REST/JSON)

Oracle PL/SQL supports UTL_HTTP and JSON processing to interact with AI APIs.

🔹 Approach 2: Use Oracle Autonomous Database AI Features

• Oracle Database 23c introduces Generative AI SQL functions (e.g., DBMS_CLOUD.GENERATE_TEXT()).
• Oracle Machine Learning (OML) allows in-database AI model training.

🔹 Approach 3: Use Oracle Integration Cloud (OIC) or API Gateway

• Oracle Integration Cloud can act as a middleware to call AI APIs and push results back to Oracle DB.

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3. Best Practices for Maximizing Free AI API Credits

To avoid hitting credit limits too soon, follow these tips:

✅ Monitor Usage – Most APIs provide dashboards to track consumption.
✅ Use Batch Processing – Instead of real-time calls, process data in bulk.
✅ Cache Responses – Store AI-generated results in Oracle tables to avoid redundant calls.
✅ Optimize API Calls – Use smaller payloads and minimal required parameters.
✅ Set Up Alerts – Get notified when credits are running low.
✅ Switch to Paid Only When Necessary – Many APIs offer pay-as-you-go after free credits expire.

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4. Sample Oracle PL/SQL Script to Call an AI API

Below is a PL/SQL script that calls the Google Vertex AI Text Generation API (using free credits) and stores the response in an Oracle table.

Prerequisites:

• Oracle Database 12c or later (with UTL_HTTP access).
• Google Cloud API Key (from Google Vertex AI).
• JSON processing enabled (DBMS_JSON package).

Step 1: Create a Table to Store AI Responses

CREATE TABLE ai_generated_text (
    id NUMBER GENERATED ALWAYS AS IDENTITY,
    input_text CLOB,
    generated_response CLOB,
    api_call_date TIMESTAMP DEFAULT SYSTIMESTAMP
);

Step 2: PL/SQL Script to Call Google Vertex AI API

DECLARE
    v_api_key VARCHAR2(1000) := 'YOUR_GOOGLE_API_KEY';
    v_project_id VARCHAR2(100) := 'YOUR_GOOGLE_PROJECT_ID';
    v_location VARCHAR2(100) := 'us-central1';
    v_model_name VARCHAR2(100) := 'text-bison@001';
    v_prompt CLOB := 'Explain Oracle Database AI features in simple terms.';
    v_response CLOB;
    v_url VARCHAR2(1000);
    v_http_req UTL_HTTP.req;
    v_http_resp UTL_HTTP.resp;
    v_status_code NUMBER;
    v_json_response CLOB;
BEGIN
    -- Construct the API URL
    v_url := 'https://us-central1-aiplatform.googleapis.com/v1/projects/' || v_project_id ||
             '/locations/' || v_location || '/publishers/google/models/' || v_model_name ||
             ':predict';

    -- Initialize HTTP request
    v_http_req := UTL_HTTP.begin_request(
        url => v_url,
        method => 'POST',
        http_version => 'HTTP/1.1'
    );

    -- Set headers
    UTL_HTTP.set_header(v_http_req, 'Content-Type', 'application/json');
    UTL_HTTP.set_header(v_http_req, 'Authorization', 'Bearer ' || v_api_key);

    -- Prepare JSON payload
    v_json_response := '{
        "instances": [
            {
                "content": "' || v_prompt || '"
            }
        ]
    }';

    -- Write payload to request
    UTL_HTTP.write_text(v_http_req, v_json_response);

    -- Get response
    v_http_resp := UTL_HTTP.get_response(v_http_req);
    v_status_code := UTL_HTTP.get_status_code(v_http_resp);

    IF v_status_code = 200 THEN
        -- Read response body
        v_response := UTL_HTTP.read_text(v_http_resp, 32767);

        -- Parse JSON to extract generated text
        DECLARE
            v_json_obj DBMS_JSON.JSON_OBJECT_T;
            v_predictions DBMS_JSON.JSON_ARRAY_T;
            v_prediction DBMS_JSON.JSON_OBJECT_T;
            v_text VARCHAR2(4000);
        BEGIN
            v_json_obj := DBMS_JSON.JSON_OBJECT_T.parse(v_response);

            v_predictions := DBMS_JSON.get_array(v_json_obj, 'predictions');
            v_prediction := DBMS_JSON.get_object(v_predictions(0), 'content');

            v_text := DBMS_JSON.get_string(v_prediction, 'parts', 0, 'text');

            -- Insert into Oracle table
            INSERT INTO ai_generated_text (input_text, generated_response)
            VALUES (v_prompt, v_text);

            DBMS_OUTPUT.put_line('AI Response: ' || v_text);
        EXCEPTION
            WHEN OTHERS THEN
                DBMS_OUTPUT.put_line('Error parsing JSON: ' || SQLERRM);
        END;

    ELSE
        DBMS_OUTPUT.put_line('API Call Failed. Status Code: ' || v_status_code);
    END

    -- Clean up
    UTL_HTTP.end_response(v_http_resp);
EXCEPTION
    WHEN OTHERS THEN
        IF UTL_HTTP.is_open(v_http_req) THEN
            UTL_HTTP.end_request(v_http_req);
        END IF
        DBMS_OUTPUT.put_line('Error: ' || SQLERRM);
END;
/

Step 3: Execute the Script

Run the script in SQL*Plus, SQL Developer, or Oracle Live SQL.
Check the ai_generated_text table for results.

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5. Alternative: Using Oracle Autonomous Database AI Features

If you’re using Oracle Autonomous Database, you can leverage built-in AI functions without external APIs:

-- Example: Generate a summary using Oracle AI (if enabled)
SELECT DBMS_CLOUD.GENERATE_TEXT(
    'Summarize the following text: ' || long_text_column,
    'text-generation-model'
) AS summary
FROM your_table;

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6. Conclusion: Start Experimenting with AI & Oracle!

Free AI API credits are a goldmine for developers looking to integrate AI into Oracle Database applications. Whether you’re:
✔ Building a chatbot with NLP APIs,
✔ Automating insights with generative AI,
✔ Optimizing SQL queries with ML,

…there’s a free tier that can help you get started.

Next Steps:

1. Sign up for free credits (Oracle, Google, AWS, or Azure).
2. Experiment with PL/SQL + AI APIs (as shown above).
3. Monitor usage and scale up when needed.

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🚀 Want More?

• Oracle AI Documentation: docs.oracle.com/en/database/
• Google Vertex AI Free Trial: cloud.google.com/vertex-ai
• AWS Bedrock Free Tier: aws.amazon.com/bedrock

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What AI features are you integrating with Oracle? Drop your thoughts in the comments! 🚀

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#OracleDatabase #AIDevelopment #FreeAIAPIs #PLSQL #GenerativeAI